This paper studies adversarial attacks on SNNs using additional attack models and shows that SNNs are not inherently robust against many few-pixel L 0 black-box ...
This paper studies adversarial attacks on SNNs using additional attack models and shows that SNNs are not inherently robust against many few-pixel L0 black-box.
It is shown that SNNs are not inherently robust against many few-pixel L0 black-box attacks and a method to defend against such attacks in SNNS is presented ...
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May 7, 2019 · We demonstrate that SNNs tend to show more resiliency compared to ANN under black-box attack scenario. Additionally, we find that SNN robustness ...
Aug 2, 2022 · Bagheri et al. [34] studied the sensitivity of SNN w.r.t. different types of encoding, when subjected to white-box adversarial attacks.
Mar 17, 2024 · Defending against black-box attacks poses a formidable challenge, primarily due to defenders' limited access to the inner parameters of the ...
Apr 26, 2024 · First, an end-to-end SNN-based image purification model is proposed to defend against adversarial attacks, including a noise extraction network ...
The recent literature considered two types of threat models: black-box and white-box attacks. In black-box attacks, the attacker is assumed to have no access to ...
May 4, 2022 · Our attack strategy consists in training a local model to substitute for the target DNN [Deep Neural Network], using inputs synthetically ...
Missing: SNN | Show results with:SNN
Oct 12, 2022 · This paper proposes a new black-box adversarial attack, which is based οn orthogonal image moments named Mb-AdA. Additionally, a corresponding ...
Missing: SNN | Show results with:SNN